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Representation Learning for Natural Language Processing

natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language;

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Representation Learning for Natural Language Processing

natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language;

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Deep Learning and Linguistic Representation

of natural language processing tasks. For some of these applications, deep learning models now approach or surpass human performance. While the;

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Deep Learning and Linguistic Representation

of natural language processing tasks. For some of these applications, deep learning models now approach or surpass human performance. While the;

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Natural Language Processing Recipes

natural language processing techniques. Identify machine learning and deep learning techniques for natural language processing and natural;

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EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing

computing and its self-learning systems that use data mining, pattern recognition and natural language processing (NLP) to mirror the way the human;

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Natural Language Processing

natural language processing in IT and web systems, describing semantic analysis of natural language queries for an object oriented database;

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Introduction to Natural Language Processing

representations and algorithms with contemporary machine learning techniques. This textbook provides a technical perspective on natural language;

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Deep Learning for Natural Language Processing

Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such;

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Natural Language Processing

of Natural Language Processing. The book will be useful for the B.Tech. and M.Tech. students.;

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Deep Learning for Natural Language Processing

various NLP tasks can take your computational algorithms to a completely new level in terms of speed and accuracy. Deep Learning for Natural;

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Python Natural Language Processing

Learning and Deep Learning techniques for efficient natural language processing * Get started with NLTK and implement NLP in your applications;

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Statistical Language Learning

needed to break the deadlock in such areas of artificial intelligence as robotics, knowledge representation, machine learning, machine;

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Statistical Language Learning

needed to break the deadlock in such areas of artificial intelligence as robotics, knowledge representation, machine learning, machine;

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Natural Language Processing & Knowledge Representation - Language for Knowledge & Knowledge for Language (OI)

of representation and inference in NL makes it the ideal level for all tasks in an intelligent computer system. NL processing combines the qualitative;

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Applied Natural Language Processing with Python

Learn to harness the power of AI for natural language processing, performing tasks such as spell check, text summarization, document;

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Natural Language Processing

These ten contributions describe the major technical ideas underlying many of the significant advances in natural-language processing;

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Neural Networks for Natural Language Processing

the practical implementations of this trending approach. Neural Networks for Natural Language Processing is a collection of innovative;

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Neural Networks for Natural Language Processing

the practical implementations of this trending approach. Neural Networks for Natural Language Processing is a collection of innovative;

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Advances in Artificial Intelligence -- SBIA 2010

, knowledge representation and reasoning; machine learning; autonomous agents and multiagent systems; natural language processing; planning and;

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Coarse-to-Fine Natural Language Processing

plausibility of grammatical constructions. This book develops a general coarse-to-fine framework for learning and inference in large statistical;

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Machine Learning for Text

intersecting topics of information retrieval, machine learning, and natural language processing. Particular importance is placed on deep learning;

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AI 2019: Advances in Artificial Intelligence

were organized in topical sections named: game and multiagent systems; knowledge acquisition, representation, reasoning; machine learning and;

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Requirements Engineering: Foundation for Software Quality

integration; visualization and representation of requirements; agile requirements engineering; natural language processing, information retrieval and;

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2nd EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing

computing and its self-learning systems that use data mining, pattern recognition and natural language processing (NLP) to mirror the way the human;

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Automatic Digital Document Processing and Management

concepts in machine learning, and providing a case study on building an intelligent system for digital document and library management; discusses;

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Machine Learning with PySpark

Build machine learning models, natural language processing applications, and recommender systems with PySpark to solve various business;

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